IoT-Based Smart Irrigation System Based Adaptive Radial Deep Neural Network (ARDNN) Algorithm Applicable for Various Agricultural Production
Keywords:
Adaptive Radial Deep Neural Network (ARDNN) algorithm, Internet of Things (IoT) based Wireless Sensor Networks (WSNs)Abstract
Agriculture Specific Land or crop monitoring-based Networking technology has played an essential role in the current farming system. Because Farmers can manage their activity even more efficiently, work and water management in irrigation systems make it possible to make decisions even when farmers are not present. The Internet of Things (IoT) monitors real-time data analysis collected through sensors and devices from each agricultural crop. The patterns of Wireless Sensor Networks (WSN), in which nodes are separated to separate data from the different crops. Sequential learning is a supervised technique using a parameter with drawbacks such as uncalculating error, Time delay, and low sensitivity data from getting sensors. To overcome these drawbacks, intelligent irrigation systems are developed using the Adaptive Radial Deep Neural Network (ARDNN) algorithm and the Internet of Things (IoT). Each sensor remains analyzed from root depth and soil water level based on the first step. Developers of the Arduino Controller have discussed utilizing IOT technology to help farmers find important environmental issues including temperature, humidity, soil moisture, and water level. The Arduino Controller analyzes different sensor setups and signal from the sensor and the Arduino controller are used to drive a water pump, which opens and closes the flow in response to the signal. Water is given to the plant's roots by a rain gun drop by drop, and when the moisture level returns to normal, the sensor recognizes it and signals the controller to cut off the water pump. Additionally, utilizing Wireless Sensor Networks (WSNs) powered by the Internet of Things (IoT), Smart Farming is used to monitor plant status. Adaptive Radial Deep Neural Network (ARDNN) algorithm aggregate classifier-based classification algorithm. The dataset will be split into training and testing data. The decision tree is constructed using the additional training dataset. To create an extra substantial prototype, the model resolve considers preparation data and then removes the weaker node from the training data before making a decision tree. The output results from gain accuracy and reliability and enables a more accurate data management system, getting proper water management and specific crops in agriculture.
Downloads
References
Alghazzawi. D, O. Bamasaq, S. Bhatia, A. Kumar, P. Dadheech and A. Albeshri, “Congestion control in cognitive IOT-based WSN network for smart agriculture”, in IEEE Access, vol. 9, pp. 151401-151420, 2021.
Alharbi. H. A and M. Aldossary, “Energy-Efficient Edge-Fog-Cloud Architecture for IoT-Based smart agriculture environment”, in IEEE Access, vol. 9, pp. 110480-110492, 2021.
Anand. T, S. Sinha, M. Mandal, V. Chamola and F. R. Yu, “Agri-Seg-Net: Deep Aerial semantic segmentation framework for IOT-assisted precision agriculture”, IEEE Sensors Journal, vol. 21, no. 16, pp. 17581-17590, 2021.
Angelin Blessy. J and A. Kumar, "Smart irrigation system techniques using artificial intelligence and IoT", Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), pp. 1355-1359, 2021.
Assaf. R and I. Ishaq, “Improving irrigation by using a cloud-based IoT System”, International Conference on promising electronic technologies (ICPET), pp. 28-31, 2020.
Bouali. E. T, M. R. Abid, E. -M. Boufounas, T. A. Hamed and D. Benhaddou, “Renewable energy integration into Cloud & IoT-Based smart agriculture”, in IEEE Access, vol. 10, pp. 1175-1191, 2022.
Chandra. S, S. Bhilare, M. Asgekar and R. B. Ramya, “Crop Water Requirement Prediction in automated drip irrigation system using ML and IoT”, 4th Biennial International Conference on Nascent Technologies in Engineering (ICNTE), pp. 1-4, 2021.
Ferrag M. A, L. Shu, X. Yang, A. Derhab and L. Maglaras, "Security and privacy for green IOT-based agriculture: review, blockchain solutions, and challenges", IEEE Access, vol. 8, pp. 32031-32053, 2020
Hadi M. S, P. Adi Nugraha, I. M. Wirawan, I. Ari Elbaith Zaeni, M. A. Mizar and M. Irvan, “IoT based smart garden irrigation system”, 4th International Conference on Vocational Education and Training (ICOVET), pp. 361-365, 2020.
Islam, Sheikh Hasan, Md. Mahmudul Islam, Md. Rafiul Hossain, Md. Faruk. “Performance analysis of IOT based solar operated smart water management system for irrigation field”, 2022.
Jain R. K, B. Gupta, M. Ansari and P. P. Ray, “IOT enabled smart drip irrigation system using web/android applications”, 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2020
Mya K. T, M. M. Sein, T. T. S. Nyunt, U. Lewlompaisarl and Y. Owada, “A Design for IoT based smart watering system using LoRa”, IEEE 9th Global Conference on Consumer Electronics (GCCE), pp. 278-279, 2020.
Aditya Sai Kilaru, Prem Madishetty, Harsha Vardhan Naidu Yamala and C V Giriraja, “Automatic remote farm irrigation system with WSN and Weather forecasting”, Journal of Physics: Conference Series, , 1st International Conference on Artificial Intelligence, Computational Electronics and Communication System (AICECS), pp. 28-30, Vol. 2161, Manipal, India, 2021.
Balasubramanian. G, R. K. Sakthivel, R. Patan, M. Sankayya, M. Daneshmand and A. H. Gandomi, "Ensemble classification and IoT-based pattern recognition for crop disease monitoring system", IEEE Internet of Things Journal, vol. 8, no. 16, pp. 12847-12854, 2021
Nandhini, R Poovizhi, S and Jose, Priyanka Ranjitha, R and Satish, Anila. “Arduino-based smart irrigation system using IOT”, 2017
Ahmed, Md Ahmed, Ezaz Ahmed, Kazi, "Automated irrigation control and security system with wireless messaging", International Conference on Informatics, Electronics and Vision, ICIEV, 2013.
Ajayi O. O, A. B. Bagula, H. C. Maluleke, Z. Gaffoor, N. Jovanovic and K. C. Pietersen, “WaterNet: a network for monitoring and assessing water quality for drinking and irrigation purposes”, IEEE Access, vol. 10, pp. 48318-48337, 2022
Bad run, Burhanuddin Manaf, Murshal, "The development of smart irrigation system with IoT, cloud, and big data", IOP Conference Series: Earth and Environmental Science, 2021
Bhat S. A and N. -F. Huang, “Big data and AI revolution in precision agriculture: survey and challenges”, IEEE Access, vol. 9, pp. 110209-110222, 2021
Gao, Z., Zhu, J., Huang, H., Yang, Y and Tan, X. “Ant colony optimization for UAV-based intelligent pesticide irrigation system”. IEEE 24th International Conference on computer-supported cooperative work in Design (CSCWD), 2021.
Jayalakshmi. B, H. Haritha, A. Maniyeri and N. Arjun, "Fuzzy-based irrigation and lighting systems for indoor farming", Second International Conference on Inventive Research in Computing Applications (ICIRCA), pp. 1183-1186, 2020.
Kundu. P, S. Debdas, S. Kundu, A. Saha, S. Mohanty and S. Samaanta, “Cloud monitoring system for agriculture using internet of things”, 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), pp. 617-622, 2020.
Mohammad Abuzanouneh, Khalil Al-Wesabi, Fahd Albraikan, Amani Al Duhayyim, Mesfer Al-Shabi, Mohammed Hilal, Anwer Hamza, Manar Zamani, Abu Muthu Lakshmi, K, “Design of machine learning based smart irrigation system for precision agriculture”, Computers, Materials and Continua, pp. 109-124, 2022.
Sami, Maira Khan, Saad Khurram, Muhammad Farooq, Muhammad Anjum, Rukhshanda Aziz, Saddam Qureshi, Rizwan Sadak, Ferhat, "A deep learning-based sensor modelling for smart irrigation system", 2022.
Sirisha. V and G. Sahitya, “Smart irrigation system for the reinforcement of precision agriculture using prediction algorithm: SVR based smart irrigation”, 6th International Conference on inventive computation technologies (ICICT), pp. 1059-1066, 2021.
Wakhare P. B, S. Neduncheliyan and G. S. Sonawane, “Automatic irrigation system based on internet of things for crop yield prediction”, International Conference on Emerging Smart Computing and Informatics (ESCI), pp. 129-132, 2020.
K. L. -M. Ang and J. K. P. Seng, “Big data and machine learning with hyperspectral information in agriculture”, IEEE Access, vol. 9, pp. 36699-36718, 2021.
Kashyap. B and R. Kumar, “Sensing methodologies in agriculture for soil moisture and nutrient monitoring”, IEEE Access, vol. 9, pp. 14095-14121, 2021.
Martínez-Ferrer. L, M. Piles and G. Camps-Valls, "Crop yield estimation and interpretability with Gaussian processes", IEEE Geoscience and Remote Sensing Letters, vol. 18, no. 12, pp. 2043-2047, 2021.
Mungale S. C., M. Sankar, D. Khot, R. Parvathi and D. N. Mudgal, “An efficient smart irrigation system for the solar system by using PIC and GSM”, International Conference on Inventive Computation Technologies (ICICT), pp. 973-976, 2020
Amin A. B, G. O. Dubois, S. Thurel, J. Danyluk, M. Boukadoum, and A. B. Diallo, “Wireless sensor network and irrigation system to monitor wheat growth under drought stress”, IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1-4, 2021
Jamroen. C, P. Komkum, C. Fongkerd, and W. Krongpha, “An intelligent irrigation scheduling system using low-cost wireless sensor network toward sustainable and precision agriculture”, in IEEE Access, vol. 8, pp. 172756-172769, 2020.
Khan. R, I. Ali, M. Zakaria, M. Ahmad, M. Imran, and M. Shoaib, "Technology-assisted decision support system for efficient water utilization: a real-time testbed for irrigation using wireless sensor networks", in IEEE Access, vol. 6, pp. 25686-25697, 2018.
Santhakumar. G, R. Vadivelu, K. Harshini, N. G. Krishanth, and G. Haritha, “Smart irrigation system for agriculture using a wireless sensor network”, 7th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 711-714, 2021.
Sarath Gopalakrishnan; Jose Waimin; Nithin Raghunathan; Saurabh Bagchi; Ali Shakouri; Rahim Rahimi; "Battery-Less Wireless Chip-less Sensor Tag for Subsoil Moisture Monitoring", IEEE Sensors Journal, 2021.
Yang. Y, “Design and application of intelligent agriculture service system with LORA-based on wireless sensor network”, International Conference on Computer Engineering and Application (ICCEA), pp. 712-716, 2020.
Pavithra. K and M. Jayalakshmi, “Analysis of precision agriculture based on random forest algorithm by using sensor networks”, International Conference on Inventive Computation Technologies (ICICT), pp. 496-499, 2020.
Qiao. M et al., "Exploiting hierarchical features for crop yield prediction based on 3-D convolutional neural networks and multichannel Gaussian process", IEEE Journal of selected topics in applied earth observations and remote sensing, vol. 14, pp. 4476-4489, 2021.
Anagha Praveen, R.Radhika, Sidharth D, Sreehari Ambat, Anjali. T, “Smart water level monitoring and management system using IoT”, 6th International Conference on Communication and Electronics Systems (ICCES), Coimbatre, India, pp. 482-487, 2021.
Risheh. A, A. Jalili and E. Nazerfard, “Smart irrigation IoT solution using transfer learning for neural networks”, 10th International Conference on Computer and Knowledge Engineering (ICCKE), pp. 342-349, 2020.
Wang C., S. He, H. Wu, G. Teng, C. Zhao and J. Li, "Identification of growing points of cotton main stem based on the convolutional neural network", IEEE Access, vol. 8, pp. 208407-208417, 2020.
Caruso A, S. Chessa, S. Escolar, J. Barba and J. C. López, “Collection of data with drones in precision agriculture: analytical model and LoRa case study”, in IEEE Internet of Things Journal, vol. 8, no. 22, pp. 16692-16704, 15 Nov.15, 2021.
Indu, R. P. Singh, H. R. Choudhary and A. K. Dubey, “Trajectory design for UAV-to-ground communication with energy optimization using genetic algorithm for agriculture application”, IEEE Sensors Journal, vol. 21, no. 16, pp. 17548-17555, 2021.
Cardoso. J, A. Glória and P. Sebastião, “Improve irrigation timing decision for agriculture using real-time data and machine learning”, International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), pp. 1-5, 2020.
Dwarakanath Ranganath M. L, S. Giridhar, K. C. Sidhartha Vineeth, “IoT based greenhouse irrigation system”, pp. 196-201, 2021.
Varshney. A, U. Sharma and B. Singh, "A grid-interactive sensorless synchronous reluctance motor drive for a solar powered water pump for agriculture and residential applications", IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), pp. 1-6, 2020
Kowsalya. M, A. Elakya, and R. Pradeep, “Solar operated PLC based automated irrigation system with fault preventer”, 7th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 1549-1551, 2021.
Misra N. N., Y. Dixit, A. Al-Mallahi, M. S. Bhullar, R. Upadhyay and A. Martynenko, “IoT, Big data, and artificial intelligence in agriculture and food industry”, IEEE Internet of Things Journal, vol. 9, no. 9, pp. 6305-6324, 2022.
N. Abdullah et al., “Towards smart agriculture monitoring using fuzzy systems”, IEEE Access, vol. 9, pp. 4097-4111, 2021.
Peraka. S, R. Sudheer, B. N. Rao, A. R. Teja and E. N. Kumar, “Smart Irrigation based on Crops using IoT”, IEEE 15th International Conference on Industrial and Information Systems (ICIIS), 2020.
V. G and S. Thangam, “Smart agriculture and role of IOT”, Third International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, pp. 1-6, 2021.
Prakash. B. R and S. S. Kulkarni, “Super smart irrigation system using internet of things”, 7th International Conference on Smart Structures and Systems (ICSSS), pp. 1-5, 2020. V. G and S. Thangam, “Smart agriculture and role of IOT”, Third International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, pp. 1-6, 2021.
Qazi. S, B. A. Khawaja and Q. U. Farooq, "IoT-equipped and AI-enabled next generation smart agriculture: a critical review, current challenges and future trends," in IEEE Access, vol. 10, pp. 21219-21235, 2022.
Rajalakshmi. K and P. Niranjana, “GSM Based irrigation system for monitoring agriculture”, 6th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 1025-1028, 2020.
Raju L. K. and V. Vijayaraghavan, “IoT and cloud-hinged smart irrigation system for urban and rural farmers employing MQTT Protocol”, 5th International Conference on Devices, Circuits and Systems (ICDCS), pp. 71-75, 2020.
Ramani. J. G, A. LakshmiPriya, S. Madhusudan, P. J. R. Kishore, M. Madhisha and U. Preethi, “Solar powered automatic irrigation monitoring system”, 6th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 293-297, 2020.
Ronad. B. F and S. B. Kumbalavati, “Performance Assessment of SPV Powered DC Irrigation Pumps with Solar Tracking Mechanism”, 3rd International Conference on Energy, Power and Environment: Towards Clean Energy Technologies, pp. 1-5, 2021.
Roy S. K, S. Misra, N. S. Raghuwanshi and S. K. Das, “AgriSens: IoT-Based Dynamic Irrigation Scheduling System for Water Management of Irrigated Crops”, IEEE Internet of Things Journal, vol. 8, no. 6, pp. 5023-5030, 15 March 15, 2021.
Sathya. A, S. Jayalalitha, R. Sabitha, R. Swetha and T. Brintha, “Application of fuzzy logic for evaluating the influence of panchagavya on tomato yield in grow bags: Fuzzy logic for tomato yield”, IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), pp. 2104-2108, 2017.
Silva.S, D. Duarte, A. Valente, S. Soares, J. Soares, and F. C. Pinto, “Augmented intelligent distributed sensing system model for precision agriculture”, Telecoms Conference (CONFTELE), pp. 1-4, 2021.
Stoyanov. L, I. Bachev and V. Lazarov, “Study of precipitation influence on the sizing of PV installation for irrigation system”, 7th International Conference on Energy Efficiency and Agricultural Engineering (EE&AE), pp. 1-4, 2020.
Talluri M. T, S. S. Arya, A. Tripathi and V. Karthikeyan, “IoT based Multipurpose uniform water showering mechanism for urban agriculture”, IEEE First International Conference on Smart Technologies for Power, Energy and Control (STPEC), pp. 1-6, 2020.
Velmurugan Balaji. S, V. Bharathi, T. Saravanan, K. Head, "An IOT-based smart irrigation system using soil moisture and weather prediction", ISSN: 2278-0181, 2020.
Divyanshu Tirkey, Kshitiz Kumar Singh, Shrivishal Tripathi, “Performance analysis of AI-based solutions for crop disease identification, detection, and classification”, Smart Agricultural Technology, Volume 5, 100238, ISSN 2772-3755, 2023.
Dimo Dimov, Johannes H. Uhl, Fabian Löw, Gezahagn Negash Seboka,"Sugarcane yield estimation through remote sensing time series and phenology metrics", Smart Agricultural Technology, Volume 2, 100046, ISSN 2772-3755, 2022.
R. Dineshkumar, M Kalimuthu, K Deepika, S Gopalakrishnan, Engineering Education with Tool Based Technical Activity (TBTA), Journal of Engineering Education Transformations, Vol. 36, 2022.
S. Manthandi Periannasamy, NC Sendhilkumar, R Arun Prasath, C Senthilkumar, S Gopalakrishnan, TT Chitra, "Performance analysis of multicast routing using multi agent zone based mechanism in MANET" International Journal of Nonlinear Analysis and Applications, Vol. 13, pp, 1047-1055, 2022.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
